With the evolution of multimodal human-machine interfaces (HMI) such as, for example, voice, gaze and/or gesture interaction there is a growing need for machine interaction recognition platforms, systems and/or methods. Hand gestures specifically may serve as a natural human interface (NUI) presenting multiple advantages, for example, eliminating and/or reducing the need for intermediator devices (such as keyboard and/or pointing devices), supporting hands free interaction, improving accessibility to population(s) with disabilities and/or providing a multimodal interaction environment.
Current solutions for identifying and/or recognizing hand(s) poses and/or gestures may exist, however they are mostly immature, presenting insufficient capabilities while requiring high computation resources for computer vision processing and/or machine learning. Such technologies may rely on full hand skeleton articulation and/or complex machine learning algorithms for classification of the hand poses and/or gestures which may make such implementations costly and unattractive for wide spread use. Furthermore accuracy of current solutions for hand(s) poses and/or gestures recognition may often prove to be insufficient for proper operation. The implementation complexity of such technologies and/or the extensive processing resources they impose on a developer for integrating them into applications, systems, platforms and/or devices may result with the developers avoiding using these technologies.